Unless, otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
In order to make informed business decisions, many organizations employ some form of data warehousing and analytical processing. A data warehouse is an electronic repository of information collected from one or more data sources. Typically, a data warehouse will contain a large cache of information, and at any given time, a particular subset of that information may be desired. To retrieve the desired data and present the data in a preferred fashion (e.g., on a computer screen or other type of graphic user interface (GUI), an analyst may perform what is known as online analytical processing (OLAP) of the warehouse data.
For example, a sales organization may desire to know which salesmen sold over a certain number units of a particular type of product during a specific time frame in a specific region of a specific country. The sales organization's data warehouse or data stores may contain sales data corresponding to all of the salesmen for multiple products across long time spans and across, the entire world. OLAP functionality provides a manner for processing the warehouse data, retrieving the desired portion of the data, and presenting the data in an appropriate form.
As organizations grow and expand, so too do their data stores. This, compounded with the increasing ease of collecting data (e.g., due to more sophisticated point-of-sale technologies, improved marketing techniques, etc.), results in exponential increases in data warehouse sizes. As a result, the cost of OLAP queries (measured by processing power and time, for example) rises, leading to poor user experience. To deal with this, more efficient data storage, more efficient data representation techniques, and more efficient OLAP routines are desired.